What Color are Commodity Prices? A Fractal Analysis
Bibliographic record
Abstract
Commodity price behavior holds much interest not only because these markets are affected by waves of speculative activity similar to security markets but more so that these commodities are linked to industries which purchase them and developing country producers which supply them. Commodity spot and future prices have thus been studied extensively. This research extends this work by employing recent fractal approaches to evaluate how the apparent random movements associated with short term behavior can also persist when examining long run behavior. We thus test for the presence of a persistent and finite variance component (i.e. long memory stationary process) as opposed to an infinite variance component (i.e. short memory nonstationary process) in a selected group of international commodity price series. Both fractal and persistent dependence hypotheses and test statistics have been employed. Estimates made of the power law exponent and of the nonintegral or fractional exponent suggest generating processes which are closer to black noise than to white, pink or brown noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".